Download Prediction, Learning, and Games by Nicolo Cesa-Bianchi, Gabor Lugosi PDF

By Nicolo Cesa-Bianchi, Gabor Lugosi

This significant new textual content and reference for researchers and scholars in desktop studying, online game thought, records and knowledge concept deals the 1st entire therapy of the matter of predicting person sequences. not like common statistical techniques to forecasting, prediction of person sequences doesn't impose any probabilistic assumption at the data-generating mechanism. but, prediction algorithms might be built that paintings good for all attainable sequences, within the experience that their functionality is often as good because the top forecasting process in a given reference type. The crucial subject matter is the version of prediction utilizing specialist suggestion, a common framework during which many comparable difficulties could be solid and mentioned. Repeated video game enjoying, adaptive facts compression, sequential funding within the inventory marketplace, sequential development research, and several difficulties are seen as situations of the specialists' framework and analyzed from a typical nonstochastic viewpoint that frequently finds new and interesting connections. outdated and new forecasting tools are defined in a mathematically distinct manner on the way to signify their theoretical obstacles and percentages.

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By assumption, takes values in [−1, 1]. ,N i=1 n ( pt , yt ) − min t=1 ≤ nη ln N + . ,N t=1 ( f i,t , yt ). 6 Scaled Losses and Signed Games Up to this point we have always assumed that the range of the loss function is the unit interval [0, 1]. We now investigate how scalings and translations of this range affect forecasting strategies and their performance. Consider first the case of a loss function ∈ [0, M]. If M is known, we can run the weighted average forecaster on the scaled losses /M and apply without any modification the analysis developed for the [0, 1] case.

19 Consider a class F of simulatable experts. Assume that the set Y of outcomes is a compact subset of Rd , the decision space D is convex, and the loss function is convex and continuous in its first argument. Show that Vn (F) = Un (F). 1. 20 Consider the discount factors βt = 1/(t + 1) and assume that there is a positive constant c such that for each n there exist outcomes y1 , y2 ∈ Y and two experts i = i such that i = argmin j ( f j,n , y1 ), i = argmin j ( f j,n , y2 ), and min y=y1 ,y2 | ( f i,n , y) − ( f i ,n , y)| ≥ c.

As the best nonuniform bounds for √ the exponential potential are obtained by choosing η = 8(ln N )/n, a natural choice for √ a time-varying exponential potential is thus ηt = 8(ln N )/t. 2, we obtain for this choice of ηt a regret bound whose main term √ is 2 (n/2) ln N and is therefore better than the doubling trick bound. More precisely, we prove the following result. 3. Assume that the loss function is convex in its first argument and takes values in [0, 1]. For all n ≥ 1 and for all y1 , . .

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